Methods and devices for determining sensing voltages

ABSTRACT

The present disclosure includes methods and devices for determining sensing voltages. One such method includes comparing data associated with a number of template distributions to data associated with a first threshold voltage distribution and a second threshold voltage distribution associated with a number of memory cells programmed to particular adjacent program states, determining an intersection of the first and second threshold voltage distributions based on a template distribution of the number template distributions which most closely compares to the first and second threshold voltage distributions, and using the determined intersection to determine a sensing voltage used to sense the number of memory cells programmed to the particular adjacent program states.

PRIORITY APPLICATION INFORMATION

This application is a Continuation of U.S. application Ser. No.13/086,984, filed Apr. 14, 2011 , issued as U.S. Pat. No. 8,503,242 onAug. 6, 2013 , the specification of which is incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates generally to semiconductor memory devicesand methods, and more particularly, to methods and devices fordetermining sensing voltages.

BACKGROUND

Memory devices are typically provided as internal, semiconductor,integrated circuits and/or external removable devices in computers orother electronic devices. There are many different types of memoryincluding random-access memory (RAM), read only memory (ROM), dynamicrandom access memory (DRAM), synchronous dynamic random access memory(SDRAM), phase change random access memory (PCRAM), and flash memory,among others.

Flash memory devices can be utilized as volatile and non-volatile memoryfor a wide range of electronic applications. Flash memory devicestypically use a one-transistor memory cell that allows for high memorydensities, high reliability, and low power consumption. Uses for flashmemory include memory for solid state drives (SSDs), personal computers,personal digital assistants (PDAs), digital cameras, cellulartelephones, portable music players, e.g., MP3 players, and movieplayers, among other electronic devices. Data, such as program code,user data, and/or system data, such as a basic input/output system(BIOS), are typically stored in flash memory devices.

Two common types of flash memory array architectures are the “NAND” and“NOR” architectures, so called for the logical form in which the basicmemory cell configuration of each is arranged. A NAND array architecturearranges its array of memory cells in a matrix such that the controlgates of each memory cell in a “row” of the array are coupled to (and insome cases form) an access line, which is commonly referred to in theart as a “word line”. However each memory cell is not directly coupledto a data line (which is commonly referred to as a digit line, e.g., abit line, in the art) by its drain. Instead, the memory cells of thearray are coupled together in series, source to drain, between a commonsource and a data line, where the memory cells commonly coupled to aparticular data line are referred to as a “column”.

Memory cells in a NAND array architecture can be programmed to a target,e.g., desired, state. For example, electric charge can be placed on orremoved from a charge storage structure of a memory cell to put the cellinto one of a number of program states. For example, a single level cell(SLC) can represent two states, e.g., 1 or 0. Flash memory cells canalso store more than two states, e.g., 1111, 0111, 0011, 1011, 1001,0001, 0101, 1101, 1100, 0100, 0000, 1000, 1010, 0010, 0110, and 1110.Such cells can be referred to as multilevel cells (MLCs). MLCs can allowthe manufacture of higher density memories without increasing the numberof memory cells since each cell can represent more than one digit, e.g.,more than one bit. For example, a cell capable of representing fourdigits can have sixteen program states.

Sensing operations, e.g., read and/or program verify operations, can usesensing voltages to determine the state of flash memory cells. However,a number of mechanisms, such as read disturb, program disturb, and/orcharge loss, e.g., charge leakage, can cause the stored charge on thecharge storage structure, e.g., the threshold voltage (Vt), of thememory cells, to change. As a result of the change in the stored charge,previously used sensing voltages, e.g., sensing voltages used prior tothe change in the stored charge occurs, may no longer provide accurateand/or reliable sensing of the memory cells. That is, previously usedsensing voltages may result in an erroneous sensing of the memory cellswhen used during subsequent sensing operations. For example, the use ofprevious sensing voltages may result in a determination that the memorycells are in a state other than the target state, e.g., a statedifferent than the target state to which the cell was programmed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph illustrating a threshold voltage distribution inaccordance with one or more embodiments of the present disclosure.

FIG. 2 is a graph illustrating a number of template distributions inaccordance with one or more embodiments of the present disclosure.

FIG. 3 is a graph illustrating the comparison of a threshold voltagedistribution and a number of template distributions in accordance withone or more embodiments of the present disclosure.

FIG. 4 is a graph illustrating intersecting threshold voltagedistributions in accordance with one or more embodiments of the presentdisclosure.

FIG. 5 is a functional block diagram of a computing system including atleast one memory system in accordance with one or more embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The present disclosure includes methods and devices for determiningsensing voltages. One such method includes comparing data associatedwith a number of template distributions to data associated with a firstthreshold voltage distribution and a second threshold voltagedistribution associated with a number of memory cells programmed toparticular adjacent program states, determining an intersection of thefirst and second threshold voltage distributions based on a templatedistribution of the number template distributions which most closelycompares to the first and second threshold voltage distributions, andusing the determined intersection to determine a sensing voltage used tosense the number of memory cells programmed to the particular adjacentprogram states.

Threshold voltage distributions associated with memory cell programstates can overlap due to programming window limitations, for instance.For example, as the memory cells are operated, e.g., programmed, read,and/or erased, the threshold voltage distribution associated with agiven program state can deform and/or widen. The deformation and/orwidening of the distribution can cause adjacent distributions tooverlap. Overlapping threshold voltage distributions can cause biterrors when sensing, e.g., reading, the memory cells. For instance,because of the distribution overlap, it may not be possible to determinea sensing voltage capable of distinguishing between the overlappingdistributions.

Sensing memory cells associated with overlapping threshold voltagedistributions with a sensing voltage that corresponds to voltages atwhich the threshold distributions intersect can reduce the number of biterrors compared to sensing the memory cells with other sensing voltages.The voltages at which threshold distributions intersect can bedetermined using software, hardware, and/or firmware on a memory systemcontroller and/or a host controller according to one or more embodimentsof the present invention.

The number of bit errors can be reduced by sensing the memory cells thatbelong to overlapping threshold voltage distributions with a sensingvoltage that corresponds to the voltage at which the distributionsintersect because the greatest number of memory cells from theoverlapping portion of the threshold voltage distributions can beassigned to the proper program state and/or voltage thresholddistribution. Locating the intersection, e.g., an intersection point,between two threshold voltage distributions cannot be accomplished usingonly the data acquired when sensing a memory cell, e.g., hard data doesnot provide information regarding the shape of the distributions. Theintersection between two voltage threshold distributions can bedetermined by comparing, e.g., cross-correlating and/or convoluting,data associated with two adjacent threshold voltage distributionsassociated with a number of memory cells programmed to particularadjacent program states with data associated with a number of templatedistributions. The threshold voltage distributions for a number ofmemory cells programmed to the adjacent program states can be determinedby dividing the range of voltages associated with a distribution into anumber of bins and determining the number of memory cells associatedwith each bin that fail.

In the following detailed description of the present disclosure,reference is made to the accompanying drawings that form a part hereof,and in which is shown by way of illustration how a number of embodimentsof the disclosure may be practiced. These embodiments are described insufficient detail to enable those of ordinary skill in the art topractice the embodiments of this disclosure, and it is to be understoodthat other embodiments may be utilized and that process, electrical,and/or structural changes may be made without departing from the scopeof the present disclosure.

As used herein, “a number of” something can refer to one or more suchthings. For example, a number of memory devices can refer to one or morememory devices. Additionally, the designators “N” and “M” as usedherein, particularly with respect to reference numerals in the drawings,indicates that a number of the particular feature so designated can beincluded with a number of embodiments of the present disclosure.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.Similar elements or components between different figures may beidentified by the use of similar digits. For example, 117 may referenceelement “17” in FIG. 1, and a similar element may be referenced as 217in FIG. 2. As will be appreciated, elements shown in the variousembodiments herein can be added, exchanged, and/or eliminated so as toprovide a number of additional embodiments of the present disclosure. Inaddition, as will be appreciated, the proportion and the relative scaleof the elements provided in the figures are intended to illustrate theembodiments of the present disclosure, and should not be taken in alimiting sense.

FIG. 1 is a graph illustrating a threshold voltage distribution 100 inaccordance with one or more embodiments of the present disclosure. Thethreshold voltage distribution 100 can be a threshold voltagedistribution for a number of memory cells at one of a number of programstates. The threshold voltage distribution 100 can overlap with anotherthreshold voltage distribution (not shown). The threshold voltagedistribution 100 can include a number of bins, 120-1, 120-2, 120-3,120-4, 120-5, 120-6, 120-7, 120-8, and 120-N. Each of the number ofbins, 120-1, . . . , 120-N, can correspond to the threshold voltage of anumber of memory cells. The number of memory cells associated with eachbin corresponds to the number in the y value of data points 104-1,104-2, 104-3, 104-4, 104-5, 104-6, 104-7, and 104-M. In FIG. 1, thenumber of memory cells associated with bins 120-1, 120-2, 120-3, 120-4,120-5, 120-6, 120-7, and 120-8 correspond to the number in the y valueof data points 104-1, 104-2, 104-3, 104-4, 104-5, 104-6, 104-7, and104-M, respectively. The number of memory cells associated with each bincan be determined by sensing the number of memory cells with a sensingvoltage that corresponds to the x value of data points the 104-1, 104-2,104-3, 104-4, 104-5, 104-6, 104-7, and 104-M.

In one or more embodiments, threshold voltage distributions for memorycells programmed to a particular program state can be determined usingsoft data associated with the memory cells. The soft data associatedwith the memory cells can be determined during a sense operationperformed on the memory cells. Soft data associated with a memory cellcan indicate, for example, a location of a threshold voltage of thememory cell within a threshold voltage distribution representing thestate to which the memory cell was programmed. Additionally, soft datacan indicate a probability of whether the threshold voltage of a memorycell corresponds to the target state to which the memory cell wasprogrammed.

In one or more embodiments, the number of data points forming athreshold voltage distribution can be greater than or equal to thenumber of data points forming the number of template distributions to becompared to the threshold voltage distribution. The data points forminga threshold voltage distribution can include a number of coordinates.The x value of the coordinate for a data point in the threshold voltagedistribution can correspond to a voltage and the y value of thecoordinate for a data point in the threshold voltage distribution cancorrespond to a number of memory cells. The data points 104-1, 104-2,104-3, 104-4, 104-5, 104-6, 104-7, and 104-M associated with thresholdvoltage distribution 100 can be stored in the memory cells of a memorydevice, for example.

FIG. 2 is a graph 200 illustrating a number of template distributions206-1, 206-2, and 206-3 in accordance with one or more embodiments ofthe present disclosure. Template distributions can include the sum ofone or more overlapping distributions that are substantially similar inshape to a threshold voltage distribution, such as threshold voltagedistribution 100 shown in FIG. 1. The one or more distributions summedto form the template distributions can be Gaussian distributions, forexample. However, the template distributions can have shapes other thanGaussian, in one or more embodiments.

In FIG. 2, template distributions 206-1, 206-2, and 206-3 include thesum of two overlapping distributions. The template distributions in FIG.2 include three templates that each are formed from the sum of twodistributions of varying overlap. Template distribution 206-1 is faultedfrom the sum of two distributions with a first overlap. For example, theoverlap of the distributions forming template distribution 206-1 can bethe largest overlap of the three template distributions. Templatedistribution 206-2 is formed from the sum of two distributions with asecond overlap. For example, the overlap of the distributions formingtemplate distribution 206-2 can be the second largest overlap of thethree template distributions. Template distribution 206-3 is formed fromthe sum of two distributions with a third overlap. For example, theoverlap of the distributions forming template distribution 206-3 can bethe smallest overlap of the three template distributions.

In one or more embodiments, multiple templates can be used forcomparison to a threshold voltage distribution. The number of templatescan include distributions with varying degrees of overlap. The values ofthe points forming the template distributions can be various values. Theabsolute values of the points forming the template distributions may notmatter, only the shape of the distributions formed by the data pointsimpact the comparison with a threshold voltage distribution in one ormore embodiments.

In one or more embodiments, a template distribution can include a numberof data points. For example, template distribution 206-1 in FIG. 2 caninclude 6 data points, template distribution 206-2 in FIG. 2 can include7 data points, and template distribution 206-3 in FIG. 2 can include 8data points. The number of data points in each template distribution candepend on the number of points used to illustrate the shape of thetemplate distribution. The number of data points in the templatedistributions can be less than or equal to the number of data points inthe threshold voltage distribution used in the comparison of thethreshold voltage distribution and the template distributions. The datapoints forming the template distributions 206-1, 206-2, and 206-3 can bestored in the memory cells of a memory device.

FIG. 3 is a graph 300 illustrating the comparison of a threshold voltagedistribution and a number of template distributions in accordance withone or more embodiments of the present disclosure. The comparison canuse data associated with two threshold voltage distributions associatedwith a number of memory cells programmed to particular adjacent programstates. The two threshold voltage distributions can be overlapping.

In one or more embodiments, the template that most closely correlateswith threshold voltage distributions will be illustrated with thehighest peak on a graph of the correlations.

In FIG. 3, the comparison illustrated includes the cross-correlation ofthe sum threshold voltage distribution 100 in FIG. 1 and anotherthreshold voltage distribution with the template distributions 206-1,206-2, and 206-3 in FIG. 2. Correlation with template 308-2 indicatesthat the threshold voltage distribution in FIG. 1 and another thresholdvoltage distribution are most closely correlated with template 206-2because the peak of the correlation with template 308-2 is the highest.Template 206-2 included the second largest overlap between the twodistributions forming template 206-2, therefore the voltage thresholddistribution in FIG. 1 and another threshold voltage distribution havean overlap similar to the two distributions forming template 206-2.

The data points of template 206-2 and the data points of the thresholdvoltage distribution 100 in FIG. 1 and another threshold voltagedistribution can be used to determine the intersection of the thresholdvoltage distribution 100 and another threshold voltage distribution. Theintersection of the threshold voltage distribution 100 with anotherthreshold voltage distribution can be used to determine the sensingvoltage that can be used to sense memory cells programmed to the programstate of the memory cells forming the threshold voltage distribution 100and/or the memory cells forming another threshold voltage distributionthat can overlap with threshold voltage distribution 100. The sensingvoltage that can be used to sense memory cells programmed to the programstate of the memory cells forming the threshold voltage distribution 100can correspond to the x value of the intersection of the thresholdvoltage distribution 100 and another threshold voltage distribution.This sensing voltage can reduce the number of bit errors when sensingthe memory cells forming the threshold voltage distribution 100 and/oranother threshold voltage distribution.

In FIG. 3, correlation with template 308-1 indicates that the sum ofthreshold voltage distribution 100 and another threshold voltagedistribution correlated less closely with template 206-1 than itcorrelated with template 206-2. Correlation with template 308-3indicates that the sum of threshold voltage distribution 100 and anotherthreshold voltage distribution correlated least closely with template206-3 because correlation with template 308-3 had the lowest peak.

In one or more embodiments, cross-correlation can include using the datapoints from the threshold voltage distributions and the templatedistributions in the following equation:

${{correl}\left( {X,Y} \right)} = \frac{\sum{\left( {x - \overset{-}{x}} \right)\left( {y - \overset{-}{y}} \right)}}{\sqrt{\sum\;{\left( {x - \overset{-}{x}} \right)^{2}{\sum\left( {y - \overset{-}{y}} \right)^{2}}}}}$

In one or more embodiments, convolution can be used to compare dataassociated with a number of template distributions to data associatedwith a threshold voltage distribution associated with a number of memorycells programmed to a particular state. The threshold voltagedistributions can be defined as a function f and the templatedistributions can be defined as functions g(1), g(2), and g(3), forexample. The convolution between the function f for the thresholddistributions and the three template distributions, g(1), g(2), and g(3)can be calculated by solving the following equation:

${\left( {f*g} \right)(n)} = {\sum\limits_{m}\;{{f(m)}{g\left( {n - m} \right)}}}$

In one or more embodiments, the function g(1), g(2), and g(3) thatproduces the best convolution with the function f for the thresholdvoltage distributions is the template that most closely resemblesoverlapping threshold voltage distributions compared to the templatedistributions.

FIG. 4 is a graph 400 illustrating intersecting threshold voltagedistributions 422, 424 in accordance with one or more embodiments of thepresent disclosure. In FIG. 4, threshold voltage distribution 422corresponds to memory cells programmed to program state L1 and thresholdvoltage distribution 424 corresponds to memory cells programmed toprogram state L2. Threshold voltage distribution 422 and thresholdvoltage distribution 424 intersect, as shown in FIG. 4. The location ofthe intersection between threshold voltage distribution 422 andthreshold voltage distribution 424 can be determined in accordance withone or more embodiments of the present disclosure.

In one or more embodiments, threshold voltage distribution 422 can bedetermined by dividing the range of threshold voltages for the memorycells corresponding to the distribution into bins and determining thenumber of memory cells associated with each bin. The number of memorycells associated with each bin can be determined by sensing the memorycells with a voltage at the boundary of each bin and determining thenumber of memory cells that fail. Threshold voltage distribution 422 canalso be determined using soft data acquired when sensing the memorycells in threshold voltage distribution 422.

In one or more embodiments, threshold voltage distribution 424 can bedetermined by dividing the range of threshold voltages for the memorycells corresponding to the distribution into bins and determining thenumber of memory cells associated with each bin. The number of memorycells associated with each bin can be determined by sensing the memorycells with a voltage at the boundary of each bin and determining thenumber of memory cells that fail. Threshold voltage distribution 424 canalso be determined using soft data acquired when sensing the memorycells in threshold voltage distribution 424.

Once threshold voltage distributions 422 and 424 are determined, the sumof threshold voltage distribution 422 and threshold voltage distribution424 can be compared to a number of template distributions, e.g.,template 206-1, template 206-2, and template 206-3 from FIG. 2, usingcross-correlation and/or convolution. The comparison between the sum ofthe threshold voltage distributions and the number of templates candetermine which of the number of templates most closely compares tothreshold voltage distributions 422 and 424. The data points fromthreshold voltage distributions 422 and 424 and the templatedistribution that most closely compared to threshold voltagedistributions 422 and 424 can be used to determine where theintersection between threshold voltage distribution 422 and thresholdvoltage distribution 424 occurs. The data points associated withthreshold voltage distributions 422 and 424 and the templatedistributions can be stored in the memory cells of a memory device. Theintersection between threshold voltage distribution 422 and thresholdvoltage distribution 424 can identify a voltage 426 that can be used asa sensing voltage for sensing the memory cells in threshold voltagedistribution 422 and/or the memory cells in threshold voltagedistribution 424. Sensing the memory cells in threshold voltagedistribution 422 with voltage 426 can reduce the number bit errors thatoccur when sensing the memory cells threshold voltage distribution 422.Sensing the memory cells in threshold voltage distribution 424 withvoltage 426 can reduce the number bit errors that occur when sensing thememory cells threshold voltage distribution 424.

FIG. 5 is a functional block diagram of a computing system 500 includingat least one memory system 544, in accordance with one or moreembodiments of the present disclosure. In the embodiment illustrated inFIG. 5, the memory system 544 can include a controller 515 and one ormore memory devices 530-1, . . . , 530-N coupled via bus 550. In thisexample, the controller 515 is external to the one or more memorydevices 530-1, . . . , 530-N. The memory devices 530-1, . . . , 530-Ncan provide a storage volume for the memory system, e.g., with a filesystem formatted to the memory devices. The controller 515 can includecontrol circuitry, e.g., hardware, firmware, and/or software. Sensingvoltages can be determined using software, hardware, and/or firmware oncontroller 515 and/or on host 540 according to one or more embodimentsof the present invention. In one or more embodiments, the controller 515can be an application specific integrated circuit (ASIC) coupled to aprinted circuit board including a physical interface and memory devices530-1, . . . , 530-N.

As illustrated in FIG. 5, a host 540 can be coupled to the memory system544. Host 540 can be a laptop computer, personal computer, digitalcamera, digital recording and playback device, mobile telephone, PDA,memory card reader, interface hub, among other host systems, and caninclude a memory access device, e.g., a processor. One of ordinary skillin the art will appreciate that “a processor” can intend one or moreprocessors, such as a parallel processing system, a number ofcoprocessors, etc.

In one or more embodiments, a physical host interface 546 can be in theform of a standardized interface. For example, when the memory system544 is used for data storage in a computing system 500, physical hostinterface 546 can be a serial advanced technology attachment (SATA),peripheral component interconnect express (PCIe), or a universal serialbus (USB), among other connectors and interfaces. In general, however, aphysical host interface 546 can provide an interface for passingcontrol, address, data, and other signals between the memory system 544and a host 540 having compatible receptors for the physical hostinterface.

The controller 515 can communicate with the memory devices 530-1, . . ., 530-N to read, write, and erase data, among other operations.Controller 515 can have circuitry that may be one or more integratedcircuits and/or discrete components. A memory controller couldselectively couple an I/O connection (not shown in FIG. 1) of a memorydevice 530-1, . . . , 530-N to receive the appropriate signal at theappropriate I/O connection at the appropriate time. Similarly, thecommunication protocol between a host 540 and the memory system 544 maybe different than what is required for access of a memory device 530-1,. . . , 530-N. Controller 515 could then translate the commands receivedfrom a host 540 into the appropriate commands to achieve the desiredaccess to a memory device 530-1, . . . , 530-N.

A memory device 530-1, . . . , 530-N can include one or more arrays ofmemory cells 512-1, 512-2, 512-M, e.g., non-volatile memory cells. Thearrays 512-1, 512-2, 512-M can be flash arrays with a NAND architecture,for example. Embodiments are not limited to a particular type of memorydevice. For instance, the memory device can include RAM, ROM, DRAM,SDRAM, PCRAM, RRAM, and flash memory, among others.

The embodiment of FIG. 5 can include additional circuitry that is notillustrated so as not to obscure embodiments of the present disclosure.For example, the memory system 544 can include address circuitry tolatch address signals provided over I/O connections through I/Ocircuitry. Address signals can be received and decoded by a row decoderand a column decoder to access the memory devices 530-1, . . . , 530-N.It will be appreciated by those skilled in the art that the number ofaddress input connections can depend on the density and architecture ofthe memory devices 530-1, . . . , 530-N.

In general, the controller 515 is responsible for converting commandpackets received from the host 540, e.g., from a PCIe bus, into commandinstructions for host-memory translation circuitry and for convertingmemory responses into host system commands for transmission to therequesting host.

Conclusion

The present disclosure includes methods and devices for determiningsensing voltages. One such method includes comparing data associatedwith a number of template distributions to data associated with a firstthreshold voltage distribution and a second threshold voltagedistribution associated with a number of memory cells programmed toparticular adjacent program states, determining an intersection of thefirst and second threshold voltage distributions based on a templatedistribution of the number template distributions which most closelycompares to the first and second threshold voltage distributions, andusing the determined intersection to determine a sensing voltage used tosense the number of memory cells programmed to the particular adjacentprogram states.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anarrangement calculated to achieve the same results can be substitutedfor the specific embodiments shown. This disclosure is intended to coveradaptations or variations of a number of embodiments of the presentdisclosure. It is to be understood that the above description has beenmade in an illustrative fashion, and not a restrictive one. Combinationof the above embodiments, and other embodiments not specificallydescribed herein will be apparent to those of ordinary skill in the artupon reviewing the above description. The scope of a number ofembodiments of the present disclosure includes other applications inwhich the above structures and methods are used. Therefore, the scope ofa number of embodiments of the present disclosure should be determinedwith reference to the appended claims, along with the full range ofequivalents to which such claims are entitled.

In the foregoing Detailed Description, some features are groupedtogether in a single embodiment for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the disclosed embodiments of the presentdisclosure have to use more features than are expressly recited in eachclaim. Rather, as the following claims reflect, inventive subject matterlies in less than all features of a single disclosed embodiment. Thus,the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment.

What is claimed is:
 1. A method, comprising: comparing, via controller,data associated with a number of template distributions to dataassociated with a first threshold voltage distribution and a secondthreshold voltage distribution of a number of memory cells programmed toparticular adjacent program states; and determining a sensing voltageused to sense memory cells based on the comparison.
 2. The method ofclaim 1, including determining an intersection of the first and secondthreshold voltage distributions based on a template distribution of thenumber template distributions which most closely compares to the firstand second threshold voltage distributions.
 3. The method of claim 1,wherein each of the number of template distributions have at least 6data points.
 4. The method of claim 1, wherein comparing data associatedwith the number of template distributions to data associated with thefirst threshold voltage distribution and the second threshold voltagedistribution includes cross-correlating the data associated with thenumber of template distributions and the data associated with the firstthreshold voltage distribution and the second threshold voltagedistribution.
 5. The method of claim 1, wherein comparing dataassociated with the number of template distributions to data associatedwith the first threshold voltage distribution and the second thresholdvoltage distribution includes convoluting the data associated with thenumber of template distributions and the data associated with the firstthreshold voltage distribution and the second threshold voltagedistribution.
 6. The method of claim 1, wherein the first thresholdvoltage distribution includes at least 6 data points and secondthreshold voltage distribution includes at least 6 data points.
 7. Themethod of claim 1, wherein the method includes sensing memory cells withthe determined sensing voltage.
 8. A method, comprising: determining afirst threshold voltage distribution associated with a number of memorycells programmed to a first program state; determining a secondthreshold voltage distribution associated with a number of memory cellsprogrammed to a second program state; comparing data associated with anumber of template distributions to data associated with the first andsecond threshold voltage distributions to determine which of the numberof template distributions most closely compares to the first and secondthreshold voltage distributions; and determining a sensing voltage usedto sense the number of memory cells programmed to the first and secondprogram states, wherein the sensing voltage is determined based on thetemplate distribution which most closely compares to the first andsecond threshold voltage distributions.
 9. The method of claim 8,wherein the number of template distributions and the first and secondthreshold voltage distributions each have a substantially similar numberof data points.
 10. The method of claim 8, wherein the sensing voltagecorresponds to an intersection of the first and second threshold voltagedistributions that is based on the template distribution which mostclosely compares to the first and second threshold voltagedistributions.
 11. The method of claim 8, wherein the number of templatedistributions include at least two template distributions with varyingoverlap between two adjacent distributions forming the at least twotemplate distributions.
 12. The method of claim 8, wherein the firstthreshold voltage distribution and the second threshold voltagedistribution are adjacent threshold voltage distributions.
 13. Themethod of claim 8, further including storing information associated withthe number of template distributions and the first and second thresholdvoltage distributions in memory cells.
 14. The method of claim 8,wherein the method includes sensing memory cells with the determinedsensing voltage.
 15. A controller, comprising: circuitry configured tocompare data associated with a number of template distributions to dataassociated with a first threshold voltage distribution and a secondthreshold voltage distribution; and circuitry configured to determine anintersection of the first and second threshold voltage distributionsbased on a template distribution of the number of template distributionswhich most closely compares to the first and second threshold voltagedistributions.
 16. The controller of claim 15, wherein the intersectionis used to determine a sensing voltage used to sense memory cellsprogrammed to a program state of the memory cells forming the firstthreshold voltage distribution.
 17. The controller of claim 16, whereinthe intersection is used to determine the sensing voltage used to sensememory cells programmed to a program state of the memory cells formingthe second threshold voltage distribution.
 18. The controller of claim15, wherein the template distribution of the number of templatedistributions which most closely compares to the first and secondthreshold voltage distributions is based on a cross-correlation of thedata associated with the number of template distributions and the dataassociated with the first threshold voltage distribution and the secondthreshold voltage distribution.
 19. The controller of claim 15, whereinthe template distribution of the number of template distributions whichmost closely compares to the first and second threshold voltagedistributions is based on convoluting the data associated with thenumber of template distributions and the data associated with the firstthreshold voltage distribution and the second threshold voltagedistribution.
 20. The controller of claim 15, including circuitryconfigured to sense memory cells forming the first threshold voltagedistribution using a voltage corresponding to the intersection of thefirst and second threshold voltage distributions.